Modern integrated circuits are enormously complex devices. Typically, even the smallest fluctuations in the materials, processes, and designs by which they are fabricated are sufficient to either degrade the operation of the device so malformed or render it completely inoperable. Because of this, there has been a tremendous effort to monitor and control the nearly innumerable count of parameters which contribute to the success of the fabrication process. The heart of such efforts has traditionally been the statistical process control engine.
Statistical process control works by receiving a stream of data in regard to a given parameter. The parameter stream is plotted, typically in an order dependent manner, although other plotting bases can also be used. The parameter may be plotted in its raw form, or in a manipulated form. The parameter stream is also mathematically manipulated to determine desired statistical values in regard to the parameter. These desired statistical values enable one to quickly detect, and often to predict, one or more of a variety of different problems with the parameter. When such a problem is detected, an investigation can be made and corrective actions can be implemented. Thus, such statistical process control has been of great benefit to the integrated circuit fabrication industry, as implemented on a wide variety of parameters.
Because of the great utility of statistical process control, there has been a concerted effort to provide as much information to the control engines as possible. For this and other reasons, equipment manufacturers have offered data collecting and reporting modules for their equipment, which modules collect some of the processing data and send it to centralized databases. Such data is generally referred to as engineering data, and such collection systems are generally referred to as engineering data collection systems.
However, there is a great amount of data that cannot be automatically gathered by such engineering data collection systems, either because equipment manufacturers have not provided the capability to do so, or because the nature of the data does not lend itself well to such automated data collection. Various efforts have been made in the past to collect such data, such as by observing the data manually, and then recording it, such as by writing it into a log on a sheet of paper. Unfortunately, such methods tend to be unreliable, inconsistent, time consuming, difficult to expand across an entire fabrication facility, and difficult to monitor. Further, entry of such information into a statistical process control system tends to have the same problems.
What is needed, therefore, is a system by which data can be more reliably gathered and entered into a data processing system.